class_docstr: Represents a compiled pipeline with transformers and fitted models.
  
  .. versionadded:: 3.5.0
code: def __init__(self, stages: Optional[List[Params]] = None):
      super(PipelineModel, self).__init__()
      self.stages = stages
  
imports: from typing import Any, Dict, List, Optional, Union, cast, TYPE_CHECKING
  import pandas
  from pyspark import keyword_only, since
  from pyspark.ml.connect.base import Estimator, Model, Transformer
  from pyspark.ml.connect.io_utils import ParamsReadWrite, MetaAlgorithmReadWrite
  from pyspark.ml.param import Param, Params
  from pyspark.ml.common import inherit_doc
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 1 to create a new PipelineModel object from inherited base classes: Model, _PipelineReadWrite with example: obj = PipelineModel(stages)